Procedural content generation in video games has a long history. Existing procedural
content generation methods, such as search-based, solver-based, rule-based and grammar …

Large Language Models (LLMs) are powerful tools, capable of leveraging their training on
natural language to write stories, generate code, and answer questions. But can they …

Several works have demonstrated the use of variational autoencoders (VAEs) for generating
levels in the style of existing games and blending levels across different games. Further …

Level generation is a central focus of Procedural Content Generation (PCG), yet deep
learning-based approaches are limited by scarce training data, ie, human-designed levels …

In this article, we introduce the concept of procedural content generation via knowledge
transformation (PCG-KT), a new lens and framework for characterizing PCG methods and …

We present Lode Encoder, a gamified mixed-initiative level creation system for the classic
platform-puzzle game Lode Runner. The system is built around several autoen-coders …

Techniques for procedural content generation via machine learning (PCGML) have been
shown to be useful for generating novel game content. While used primarily for producing …

In recent years, machine learning (ML) systems have been increasingly applied for
performing creative tasks. Such creative ML approaches have seen wide use in the domains …

Existing methods of level generation using latent variable models such as VAEs and GANs
do so in segments and produce the final level by stitching these separately generated …

Prior research has shown variational autoencoders (VAEs) to be useful for generating and
blending game levels by learning latent representations of existing level data. We build on …